Self-Location of a Mobile Robot with uncertainty by cooperation of an heading sensor and a CCD TV camera

Transcription

1 Self-oation of a Mobile Robot ith unertainty by ooperation of an heading sensor and a CCD TV amera E. Stella, G. Ciirelli, A. Distante Istituto Elaborazione Segnali ed Immagini - C.N.R. Via Amendola, 66/5-706 Bari (Italy) stella@iesi.ba.nr.it Abstrat Goal-oriented navigation of a mobile robot by landmark based tehniques is a straightforard and suitable approah. Most of methods, normally, give an estimation of the position and orientation of the vehile, but, often, they are not able to provide a good estimate of the unertainty in the measurement. That information is useful in appliation here multisensor fusion is requested. Our method permits to determine the vehile loation and relative unertainty, hen its orientation is obtained by an heading sensor, using a visual landmark based method. The approah is straightforard and suitable for real time performane on general purpose hardare. Experimental results are provided by implementation on our autonomous mobile vehile SAURO. Keyords: Camera oalization, Navigation, Autonomous mobile vehile, Self-oation. The researh is partially supported by Progetto Speiale Robotia (Comitato Sienze Fisihe) of the C.N.R.. Introdution Self-loation is the apability of an autonomous mobile vehile to determine its position in an environment. Visual approahes to self-loation are suitable for navigating a mobile robot in indoor ontexts. To lasses of methods an be onsidered: Absolute tehnique: are those approahes providing an estimation of the loation and orientation of the vehile using artifiial or natural landmarks hose positions in the environment are a-priori knon [, 4, 7]. Relative tehnique: are those methods here only the relative motion of the vehile is estimated (rotation and translation) so for goal oriented appliations the absolute loation of the start point must be knon. Those tehniques, hoever, do not require any a-priori knoledge about the environment [6,, ]. Methods in both lasses ork ith a good performanes (referred to exeution time) and provide an aeptable estimation of the vehile loation and orientation. Most of them, hoever, have a fundamental drabak: they do not provide an estimation of the unertainty in the vehile loation and orientation. In the last years, a ommon tendeny of roboti researhers has been to analyze the problems of the mobile vehiles in term of multisensor fusion frameork, so the estimation of unertainty of sensor measurements has played an important rule. In this paper e propose a straightforard approah to self-loation of a mobile robot by loalizing the Center-ofprojetions (CP) of the amera hen the orientation of its optial axis is given. In fat, using an heading sensor (e have used a ompass), the orientation of the vehile an be reovered, so three landmarks are enough to estimate loation of the amera and relative unertainty. The experimental setup is made up by three beaons (infrared EDS) used as landmarks at knon positions in the environment. Using the exellent angular resolution of CCD ameras, visual angles are evaluated and, as desribed in set., the loation of the CP and optial axis orientation are reovered. The visual angle of a landmark is the angle formed by the ray from the CP to the landmark and the optial axis (see fig.). The tehnique used to estimate the visual angle is similar to [3]. We have used artifiial beaons, in order to speed up the searh for the landmarks in the image, but natural features ould be onsidered. The tehnique is desribed in setion., the estimation of unertainty is explained in set.3.. The experimental setup is desribed in set. 4 and, finally, the results on self-loation of a mobile robot are shon.

2 P P Y Y (Optial axis) Image Plane α Image plane Figure. 3D amera geometry. CP self-loation tehnique α et P ( ; Y ; ), P ( ; Y ; ) be landmarks in orld referene system O (fig.). et V ( v ; Y v ; v ) be the unknon oordinates of the enter-of-projetion (CP) of the amera. In the ontext of autonomous robot navigation the determination of the loation of the CP in the? plane is all important, so the loation problem is redued to a to-dimensional one. et ( ; ), ( ; ), and CP ( p ; p ) be the projetions onto the plane? of P and P and V (fig.). If the heading angle is assumed to be knon (for example obtained by an heading sensor: gyro, ompass, et.) and the visual angles and an be reovered, then the CP position an be univoally determined as intersetion of the to straight lines r and r, passing among landmarks and, respetively. Considered the parametri equation of a straight line: z = mx + p () here m, the slope of the straight line, an be estimated as: m = atan( + i ) i = ; () hile p the interept of the straight line an be estimated onsidering that the line pass through a landmark. So, summarizing r an be estimated by resolving the system of to equations: = m + p (3) m = atan( + ) Analogously, the straight line r an be estimated and so the CP loation is given by resolving the system of the equations of the to straight lines. The visual angles and an be estimated using the exellent angular resolution of the CCD ameras. Considering the fig.: Figure. D amera geometry = atan( u f ) = atan( u f ) (4) here f is the foal length of the amera and u and u are the distanes on the x-axis (on the image plane) of the projetions of the landmarks from the prinipal point (intersetion of optial axis and image plane). Considering that, u and u in (4), are pixel oordinates referred to the CCD hip (not to the frame grabber), their estimation needs of the sale fator. In fat, as shon in [0]: u k = (p k x? C x) d x s x N x N fx (5) here p k x is the pixel x-oordinate referred to the frame grabber, C x is the prinipal point x-oordinate, s x is the sale fator given by different sampling frequenies of the TV amera and the frame grabber ADC, N x represents the number of the CCD elements in x-diretion hile N fx is the length of the image line in the frame grabber, d x is the distane enter-to-enter of to adjaent d elements in x- diretion. While d x ; N x and N fx are parameters desribed by the TV amera and Frame Grabber manufatures, the prinipal point, foal length, sale fator and lens distortion are amera intrinsi parameters that an be reovered only by amera alibration. [0, 8, 9, 5]. In our experiment, e have used the tehnique desribed by Tsai in [0] to perform amera alibration. 3. Unertainty estimation In order to estimate unertainty e have used some geometri onsideration. In fat, three landmarks are

3 3 G A F B C D E Figure 3. Unertainty evaluation geometry used and for eah pair of them an estimation of loation has been obtained, globally, e reovered 3 estimates. In fig.3 those 3 estimates are labelled ith A( A ; A ), B( B ; B ) and C( C ; C ). Considering the triangle ABC and min = min( A ; B ; C ), max = max( A ; B ; C ), min = min( A ; B ; C ), max = max( A ; B ; C ), the retangle DEF G inluding ABC has been determined. The retangle has the sides parallel to the axis of the orld referene system and the vertexes oordinates are: D( min ; min ), E( max ; min ), F ( max ; max ) and G( min ; max ). The unertainty has been represented by the ellipse irumsribed to the retangle DEF G as dran in fig.3. For multisensor fusion proposal using frameork as Extended Kalman Filter, a TV amera loation standard deviation independently in - diretion and -diretion an be preferred. So, e propose to assume the enter of the ellipse as the loation estimation and the axis length as standard deviation respetively in and diretions. The unertainty in TV amera orientation is not diretly onneted to the visual loation method but depends exlusively by the unertainty of the heading sensors and an be estimated on statistial basis using repetitively readings of the heading sensor at knon angles. Figure 4. Vie of environment and position of landmarks 4. Experimental results The method for self-loation has been tested on our mobile robot SAURO, shon in fig.5. SAURO is based on a TRC ABMATE mobile base ontrolled by an on-board VME-bus based omputer. The imaging system is the VME board IPP ETEC frame grabber. The proessing is performed on a Motorola board. Figure 5. Our mobile vehile SAURO

4 The TV amera used is an ADIMEC M5 mounted on a RS3 ontrolled pan-tilt motorized head. The aquired images are pixels. The heading sensor used is a KVH C00 fluxgate ompass onneted by an RS3 to the Motorola board. The C00 ompass provides on-board firmare to estimate orretion oeffiients to orret the sensor readings respet of magneti influene of metalli omponent of the mobile vehile. The heading angle of the TV amera (referring to fig. (orientation of the optial axis) is so reovered, using ompass as the sum of to angles: = +! (6) Figure 6. a) Experimental results of self-loation on a path. On path are shon the positions here self-loation is applied and the unertainty is represented by ellipses here is the angle beteen the -axis of the orld referene system and the magneti NORTH diretion deteted by the ompass, hile! is the urrent pan angle of the PAN- TIT head. In order to verify the reliability of self-loation in navigation ontext, SAURO as asked to perform a path to a goal position. The path onsisted of the segments shon in fig.6 (Real loations). The navigation strategy is as follos:. The vehile starts from an unknon position ith an unknon orientation.. Using the pan-tilt head the landmarks are searhed for. Then, by self-loation the mobile vehile estimates its position and orientation and relative unertainty in the orld referene system. 3. The rotation and translation in order to go to the next stop position are evaluated and then the vehile moves on. landmark Real Estimated unert. distanes oation oation 963 x = x = x = z = z = z = x = x = x = 79.8 z = z = z = x = x = 45.3 x = z = z = z = x = x = x = z = z = z = x = x = x = z = z = z = x = x = x = z = z = z = x = x = x = z = z = z = x = x = x = z = z = z = Table. Path performed by SAURO. The foal length of amera as 6 mm. Data are in mm. The desribed proedure is repeated until the vehile reahes the goal. The positions estimated by self-loation ompared ith real positions are shon in fig.6, hile in tab. the same data are tabulated. The robot loation as verified using a D-50 NIKON TOTA STATION (Theodolite) that an estimate the oordinates of a point in spae ith an error of 5 mm. The D-50 as also used to determine the

5 3D loations of the landmarks in the orld referene system. The fig.6 shos that the error in self-loation depends on distane from landmarks: the nearer landmarks, the less the error in robot loation. The axis of the ellipse represent the standard deviation of the error by determining the amera loation by the visual self-loation method. Fig.6 shos that error gros ith both: the distane from the landmarks and from vie angle of the amera. In fat, the distane, like vie angle, modify the appearane of the landmarks on the image plane of the amera, determining a groth of the unreliability in the visual angle estimation. The self-loation estimation time is about 40 ms, but the global time depends on the time taken by the pan-tilt head to move in order to permit to the amera to see all landmarks. The speed of the head is 300 deg/se. In order to redue the global time, the head starts to move during the motion of the vehile from a path position to the next. This is possible beause the path positions are a-priori knon. Furthermore, no a speifi onfiguration of the landmarks is requested for self-loation, so the EDs are plaed in the environment in order to have their maximum visibility. Hoever, in real ontext or to redue the self-loation error or to resolve unforeseen landmark olusion situations, e have hosen to use more setup of landmarks. So, beause the path is knon, at eah position the vehile ould hoose that setup of landmarks ill produe loest unertainty in amera loalization. 5. Conlusions This paper desribes a ne method for amera loation suitable for mobile robot navigation in indoor environment orking on a omputer. Very promising results have been obtained from tests on our SAURO mobile robot. The next step in our researh is to substitute natural landmarks for the EDs and to develop a mobile robot apable of operating as a transportation system in any indoor environment. [4] A.Distante E.Stella. Self-loation of a mobile robot by estimation of amera parameters. Robotis and Autonomous Systems - Elsevier, 5:79 87, 995. [5] P.A.Beardsley et al. The orretion of radial distortion in images. Tehnial report 896/9,Department of Engineering Siene, Univ. of Oxford (UK). [6] H.P.Morave. The stanford art and the mu rover. Proeeding of IEEE, pages , July 983. [7] H.S.Cho J.H.Kim. Experimental investigation for a mobile robot s position by linear sanning of a landmark. Robotis and Autonomous Systems - Elsevier, 3(), June 994. [8] M.Herniou J.Weng, P.Cohen. Camera alibration ith distortion models and auray evaluation. IEEE PAMI, 4(0), 99. [9] R.Y.Tsai R.K.enz. Tehniques for alibration of sale fator and image enter for high auray 3d vision metrology. Pro. IEEE Int. Conf. Robotis Automat., Marh 987. [0] R.Y.Tsai. A versatile amera alibration tehnique for high-auray 3d mahine vision metrology using offthe-shelf tv ameras and lenses. IEEE J. Robotis Automat., RA-3(4), 987. [] T.D Orazio A.Distante G.Attolio.Caponetti E. Stella. A vision algorithm for mobile vehile navigation. SPIE Proeedings on Advanes in Intelligent Roboti systems, November 99. Referenes [] A.Kak A.Kosaka. Fast vision-guided mobile robot navigation using model-based reasoning and predition of unertainties. CVGIP:Image Understanding, 56(3), November 99. [] O.Binford D.J.Kriegman, E.Triendl. Stereo vision and navigation in buildings for mobile robots. IEEE trans. on Robotis and Automation, 5(6):79 803, 989. [3] E.Krotkov. Mobile robot loalization using a single image. Pro. of IEEE Conf. on Robotis and Automation, 989.